Tagged: estimate

Point Estimation of Parameters

The objective of point estimation of parameters is to obtain a single number from the sample which will represent the unknown value of the parameter.

Practically we did not know about the population mean and standard deviation i.e population parameters such as mean, standard deviation etc. But  our goal is to measure (estimate) the mean and standard deviation of population we are interested from sample information to save time, cost etc.  This can be done by estimating the sample mean and standard deviation as a best guess for the true population mean and standard deviation.  We can call this estimate as “best guess” and termed as a “point estimateas it a single number summarized one.

A Point Estimate is a statistic (a statistical measure from sample) that gives a plausible estimate (or possible a best guess) for the value in question.

$\overline{x}$ is a point estimate for $\mu$ and s is a point estimate for $\sigma$.

Or we can say that

A statistic used to estimate a parameter is called a point estimator or simply an estimator. The actual numerical value which we obtain for an estimator in a given problem is called an estimate.

Generally symbol $\theta$ (unknown constant) is used to denote a population parameter which may be a proportion, mean or some measure of variability. The available information is in the form of a random sample $X_1,X_2,\cdots, X_n$ of size n drawn from the population. We wish to formulate a function of the sample observations $X_1,X_2,\cdots,X_n$; that is, we look for a statistic such that its value computed from the sample data would reflect the value of the population parameter as closely as possible. The estimator of $\theta$ is commonly denoted by $\hat{\theta}$. Different random samples usually provide different values of the statistic $\hat{\theta}$ having its own sampling distribution.

Note that Unbiasedness, Efficiency, Consistency and Sufficiency are the criteria (statistical properties of estimator) to identify that whether a statistic is “good” estimator.

Application of Point Estimator Confidence Intervals

We can build interval with confidence as we are not only interested in finding the point estimate for the mean, but also determining how accurate the point estimate is. Here the Central Limit Theorem plays a very important role in building confidence interval.  We assume that the sample standard deviation is close to the population standard deviation (which will almost always be true for large samples). The standard deviation of the sampling distribution of estimator (here for mean) is

\[\sigma_x \approx \frac{\sigma}{\sqrt{n}}\]

Our interest is to find an interval around $\overline{x}$ such that there is a large probability that the actual (true) mean falls inside the computed interval.  This interval is called a confidence interval and the large probability is called the confidence level.


Suppose that we check for clarity in 50 locations in Lake and discover that the average depth of clarity of the lake is 14 feet with a standard deviation of 2 feet.  What can we conclude about the average clarity of the lake with a 95% confidence level?


variable x (depth of lack at 50 location) can be used to provide a point estimate for $\mu$ and s to provide a point estimate for s. To answer how accurate is x as a point estimate, we can construct a 95% confidence interval for $\mu$ as follows.

normal curve

Draw the picture like given below and use the standard normal table to find the z-score associated to the probability of .025 (there is .025 to the left and .025 to the right i.e. two tailed case).

z-score for 95% confidence level is about ±1.96.

\pm 1.96&=\frac{\overline{x}-\mu}{\frac{2}{\sqrt{n}}}\\
\overline{x}-14&=\pm 0.5488

Note that $Z\frac{\sigma}{\sqrt{n}}$ is called the margin of error.

The 95% confidence interval for the mean clarity will be (13.45, 14.55)

In other words there is a 95% chance that the mean clarity is between 13.45 and 14.55.

In general if z is the standard normal table value associated with given level of confidence then a $\alpha$% confidence interval for the mean is

\[\overline{x} \pm Z_{\alpha}\frac{\sigma}{\sqrt{n}}\]

See more at WikiPedia about Point Estimator of Parameters

Advantages of Interval Estimation over Point Estimation

The problem with using a point estimate is that although it is the single best guess you can make about the value of a population parameter, it is also usually wrong. Interval estimate overcomes this problem using interval estimation technique which is based on point estimate and margin of error.

Interval Estimation
  • A major advantage of using interval estimation is that you provide a range of values with a known probability of capturing the population parameter (e.g., if you obtain from SPSS a 95% confidence interval you can claim to have 95% confidence that it will include the true population parameter.
  • An interval estimate (i.e., confidence intervals) also helps one to not be so confident that the population value is exactly equal to the single point estimate. That is, it makes us more careful in how we interpret our data and helps keep us in proper perspective.
  • Actually, perhaps the best thing of all to do is to provide both the point estimate and the interval estimate. For example, our best estimate of the population mean is the value $32,640 (the point estimate) and our 95% confidence interval is $30,913.71 to $34,366.29.
  • By the way, note that the bigger your sample size, the more narrow the confidence interval will be.
  • If you want narrow (i.e., very precise) confidence intervals, then remember to include a lot of participants in your research study.
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